Artificial Intelligence And Machine Learning: What's The Difference?

Tobi Bamidele

AI and ML have been buzzwords on the Internet and other media channels for some time now, constantly attracting negative and positive attention from society at large. But the two terms often mean different things to different people. Coming across AI or ML can evoke fear and uncertainty, as some experts have expressed concerns about the risks associated with both terms.

In fact, AI and ML are already an intrinsic part of our everyday lives, even in ways that many people are unaware of. They have greatly influenced how we interact with others and the ways by which we adopt technology. Thanks to the likes of Siri and Cortana, simple tasks such as searching the web or setting the alarm on a smartphone can be performed via the user’s voice. In addition, Facebook users now have access to a newsfeed that is tailored based on their previous activities.

According to Prismtech, tech companies are “rising to the growth of AI by making huge investments into its research.” Apart from having a great impact on mobile devices, the applications of AI can be also seen in the Internet of Things. The two are deeply connected as the data provided by IoT is used by AI to improve its performance. AI is becoming mainstream in business, leading to a renewed interest to develop and adopt it into business processes.

history of artificial intelligence
Image credit: Nvidia

The graph above illustrates the relationship between AI, ML, and deep learning, a subset of ML. It’s common for many people to use AI and ML interchangeably, but slight differences still exist between them. This leads us to the big question: What exactly is the major difference between AI and ML?

Artificial intelligence

AI basically refers to the ability of machines to think like humans. The process involves developing a computer system that is able to perform tasks traditionally done by humans better and more efficiently. Alan Turing published a paper in 1950 which raised the question of whether machines can really think, resulting in the proposal of the “Turing test.”

The concept of AI isn’t new at all; it was coined by John McCarthy during an academic conference in 1956. This instantly opened the door to various research projects on AI, but interest quickly fizzled out, until recently remaining a mere concept. Today, AI is developing at an unprecedented rate due to Big Data and cloud computing, which makes it easy to store of vast amounts of data.

According to experts, there are different forms of AI: narrow AI and general AI. Currently we utilize narrow AI, which can carry out a narrow range of basic tasks better than humans, but it’s deficient in other tasks. Take, for instance, a machine that is perfect at offering web-based recommendations to consumers and nothing else.

As for general AI, the AGI society defines this as “an emerging field aiming at the building of thinking machines; that is, general-purpose systems with intelligence comparable to that of the human mind (and perhaps ultimately well beyond human general intelligence).”

Machine language

There are different definitions for ML, and some can be confusing. Arthur Samuel defined ML as “a field of study that gives computer the ability to learn without being explicitly programmed.” The concept of ML involves training a machine to learn from a large amount of data that is fed into it using algorithm. Data mining is similar to ML; both use algorithms to search for patterns in a given set of information, but ML adapts its program pattern based on what it learns.

ML plays a huge role in tools that companies use to analyze data. ML is, to date, the most effective approach to achieving AI. It is possible to arrive at AI without ML, but other processes are complex and time-consuming.

Artificial Neural Network (ANN) is a computer system that has been designed to process data in ways that are similar to the ways a human brain works. It is the basis for Deep Learning, a building system that uses Deep Neural Network on a large set of data. AI, ML, and Deep Learning all rely on Big Data.

The future of AI lies in Deep Learning as it is already making many practical applications of ML possible. One example is image recognition via Deep Learning, which has been proven to be more accurate than when it is done by humans. We are already in an era in which technology is gradually adapting to the needs of human beings. Even as some experts raise concerns, research is ongoing concerning the safety of General AI. From handling of some dangerous jobs to holding the key to the cure of many life-threatening diseases, the future of AI has endless possibilities.

For more insight on machine learning, see Turning Machine Learning Into Intelligence That Matters.

About Tobi Bamidele

Tobi Bamidele is interested in the evolving tech landscape and tech advancements such as the Internet of Things and Big Data. He is also a contributor to a variety of tech publications including Tech.Co and Right Mix Marketing.